Project Summary A multitude of common genetic variants influencing risk for neuropsychiatric disorders (e.g., schizophrenia, major depressive disorder, and Alzheimer?s disease) have recently been identified and replicated, providing a foothold into the causes of these disorders. The critical next step in neuropsychiatric genetics is to move from a risk locus in the genome to an understanding of how this genetic variation influences molecules, cells, and circuits of the brain, leading to complex disorders. Many datasets, including those generated by our own labs, have established direct links between genotype and human brain traits at multiple levels of biology (molecular: chromatin accessibility, expression; cellular: morphology; circuit: gross brain structure), termed quantitative trait loci (QTLs). Here, we will integrate QTLs across multiple levels of biology in order to statistically prioritize causal pathways by which genetic variation creates risk for complex neuropsychiatric disorders. Causal modeling goes well beyond previous co-localization work, as it allows the prioritization of expensive functional validation experiments for cellular or molecular changes that are a cause of the disorder, rather than those that are a consequence or independent of the disorder. It additionally allows inference of key experimental parameters including cell-type and developmental time period. Finally, causal inference when combined across multiple levels of biology and multiple disorder risk loci allows for assessment of convergence at a biological level, cell-type, or developmental time period, which is critical information for therapeutic targeting. We will leverage the computational and statistical frameworks of Bayesian probabilistic networks and causal inference in a new framework that utilizes association summary statistics, as well-powered multi-level data collected on the same individuals is almost always infeasible. Subsequently, we will experimentally validate the molecular predictions of our model using epigenetic engineering in primary human neural progenitor cells, and in turn revising the computational models. Prioritizing causal molecular pathways of disorder associated variants, and identifying the relevant cell-type and developmental stage will increase the success rate of validation experiments and shed light on mechanisms of neuropsychiatric disorders in an unbiased manner.